Notebooks/Table-kb-topperforming_electronic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bcfd990c",
   "metadata": {},
   "source": [
    "#### AI/ML directed high-throughput virtual screening of probes to selectively bind with PFOS using SDS as interferent\n",
    "#### Siva Dasetty, Max Topel | Ferguson Lab\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2bbdb11",
   "metadata": {},
   "source": [
    "## imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9104e819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'calculateFES' from '/scratch/midway2/sivadasetty/pfas/analysis/github/activeLearningPFASLinear/Notebooks/../Codes/calculateFES.py'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import os.path\n",
    "import sys\n",
    "import importlib\n",
    "import yaml\n",
    "\n",
    "import subprocess\n",
    "\n",
    "from time import time\n",
    "from datetime import datetime\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "\n",
    "import scipy\n",
    "\n",
    "import rdkit\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit import DataStructs\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from rdkit.Chem.Draw.MolDrawing import MolDrawing, DrawingOptions\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit.Chem.Draw import rdMolDraw2D\n",
    "from rdkit.Chem import Descriptors\n",
    "from rdkit.Chem.Fragments import *\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import StrMethodFormatter\n",
    "from matplotlib import rcParams\n",
    "import matplotlib as mpl\n",
    "from matplotlib  import cm\n",
    "from PIL import Image\n",
    "from matplotlib import colors\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from matplotlib.offsetbox import (TextArea, DrawingArea, OffsetImage,\n",
    "                                  AnnotationBbox)\n",
    "from matplotlib.cbook import get_sample_data\n",
    "\n",
    "import multiprocessing\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "sys.path.append(\"../Codes/\")\n",
    "\n",
    "\n",
    "import utils_activeLearning_Oliver as ut\n",
    "\n",
    "import utils_functions as utils_functions\n",
    "#import utils_figures as utils_figures\n",
    "\n",
    "importlib.reload(ut)\n",
    "importlib.reload(utils_functions)\n",
    "#importlib.reload(utils_figures)\n",
    "\n",
    "import selfies as sf\n",
    "\n",
    "from oliver_vae.VAE import VAE_encode, VAE_decode\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "import copy\n",
    "\n",
    "import oapackage\n",
    "\n",
    "import importlib\n",
    "import calculateFES\n",
    "importlib.reload(calculateFES)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d393c75a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bcbcb5be",
   "metadata": {},
   "source": [
    "## plot settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3247578f",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({'font.size': 30})\n",
    "\n",
    "# time stamp\n",
    "now = datetime.now()\n",
    "now = str(now)[:10]\n",
    "#figures_dir = './Manuscript_Figures_JCED/{}'.format(str(now)).replace(':','-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94cc3825",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8e40efc7",
   "metadata": {},
   "source": [
    "#### extract delta G and Kb from data in each cycle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7798c920",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### read previously computed and newly computed delta G data\n",
    "\n",
    "simdir = '../Data/GPR_training_data'\n",
    "\n",
    "\n",
    "new_Kb_data_cycle_PFOS = []\n",
    "new_Kb_data_cycle_PFOS_SD = []\n",
    "new_Kb_data_cycle_SDS = []\n",
    "new_Kb_data_cycle_SDS_SD = []\n",
    "\n",
    "new_deltaG_data_cycle_PFOS = []\n",
    "new_deltaG_data_cycle_PFOS_SD = []\n",
    "new_deltaG_data_cycle_SDS = []\n",
    "new_deltaG_data_cycle_SDS_SD = []\n",
    "\n",
    "smiles_cycle = []\n",
    "\n",
    "for analyte in ['PFOS', 'SDS']:\n",
    "    \n",
    "    for c in range(0,11):\n",
    "        \n",
    "        \n",
    "        new_deltaG_data = pd.read_csv(simdir+'/wsmiles_deltaG-linearProbesBlockArea-b3-pbmd-4cvs_cycle_'+str(c)+'.txt', delim_whitespace=True, \n",
    "                              names=[\"Method\", \"Analyte\", \"lprobe\", \"lflourines\", \"lhydrogen\", \"hhydrogen\", \n",
    "                                     \"hcarbons\", \"Probe\", \"Avg\", \"SD\", \"smiles\", \"formula\"], comment=\"#\")\n",
    "\n",
    "        if analyte == 'PFOS':\n",
    "            new_deltaG_data_cycle_PFOS.append(new_deltaG_data[(new_deltaG_data[\"Analyte\"] == analyte)].Avg.values)\n",
    "            new_deltaG_data_cycle_PFOS_SD.append(new_deltaG_data[(new_deltaG_data[\"Analyte\"] == analyte)].SD.values)\n",
    "        else:\n",
    "            new_deltaG_data_cycle_SDS.append(new_deltaG_data[(new_deltaG_data[\"Analyte\"] == analyte)].Avg.values)\n",
    "            new_deltaG_data_cycle_SDS_SD.append(new_deltaG_data[(new_deltaG_data[\"Analyte\"] == analyte)].SD.values)\n",
    "                    \n",
    "        new_Kb_data = pd.read_csv(simdir+'/wsmiles_Kb-linearProbesBlockArea-b3-pbmd-4cvs_cycle_'+str(c)+'.txt', delim_whitespace=True, \n",
    "                              names=[\"Method\", \"Analyte\", \"lprobe\", \"lflourines\", \"lhydrogen\", \"hhydrogen\", \n",
    "                                     \"hcarbons\", \"Probe\", \"Avg\", \"SD\", \"smiles\", \"formula\"], comment=\"#\")\n",
    "\n",
    "        if analyte == 'PFOS':\n",
    "            new_Kb_data_cycle_PFOS.append(new_Kb_data[(new_Kb_data[\"Analyte\"] == analyte)].Avg.values)\n",
    "            new_Kb_data_cycle_PFOS_SD.append(new_Kb_data[(new_Kb_data[\"Analyte\"] == analyte)].SD.values)\n",
    "            smiles_cycle.append(new_Kb_data[(new_Kb_data[\"Analyte\"] == analyte)].smiles.values)\n",
    "        else:\n",
    "            new_Kb_data_cycle_SDS.append(new_Kb_data[(new_Kb_data[\"Analyte\"] == analyte)].Avg.values)\n",
    "            new_Kb_data_cycle_SDS_SD.append(new_Kb_data[(new_Kb_data[\"Analyte\"] == analyte)].SD.values)\n",
    "                    \n",
    "            \n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09c7c33f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1bf61a40",
   "metadata": {},
   "source": [
    "#### kb based sensitivity and selectivity calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "598d5215",
   "metadata": {},
   "outputs": [],
   "source": [
    "cycles=11\n",
    "\n",
    "sensitivity_cycle = []\n",
    "sensitivity_cycle_SD = []\n",
    "sds_sensitivity_cycle = []\n",
    "sds_sensitivity_cycle_SD = []\n",
    "selectivity_cycle = []\n",
    "selectivity_cycle_SD = []\n",
    "\n",
    "kT = 1 #300*0.008314\n",
    "for c in range(cycles):\n",
    "\n",
    "    sensitivity_cycle.append(-np.array(new_deltaG_data_cycle_PFOS[c]).flatten()/kT)\n",
    "    sensitivity_cycle_SD.append(np.array(new_deltaG_data_cycle_PFOS_SD[c]).flatten()/kT)\n",
    "\n",
    "    sds_sensitivity_cycle.append(-np.array(new_deltaG_data_cycle_SDS[c]).flatten()/kT)\n",
    "    sds_sensitivity_cycle_SD.append(np.array(new_deltaG_data_cycle_SDS_SD[c]).flatten()/kT)\n",
    "    \n",
    "    selectivity = np.array(new_deltaG_data_cycle_PFOS[c]).flatten() - np.array(new_deltaG_data_cycle_SDS[c]).flatten()\n",
    "    selectivityError = np.sqrt(np.array(new_deltaG_data_cycle_PFOS_SD[c]).flatten()**2 + np.array(new_deltaG_data_cycle_SDS_SD[c]).flatten()**2)\n",
    "    \n",
    "    selectivity_cycle.append(-selectivity/kT)\n",
    "    selectivity_cycle_SD.append(selectivityError)\n",
    "    \n",
    "catenated_sensitivity = []\n",
    "catenated_sensitivity_SD = []\n",
    "catenated_SDS_sensitivity = []\n",
    "catenated_SDS_sensitivity_SD = []\n",
    "catenated_selectivity = []\n",
    "catenated_selectivity_SD = []\n",
    "\n",
    "catenated_smiles = []\n",
    "for c in range(cycles):\n",
    "    \n",
    "    _temp = sensitivity_cycle[c]\n",
    "    _temp_SD = sensitivity_cycle_SD[c]\n",
    "    _temp_SDS = sds_sensitivity_cycle[c]\n",
    "    _temp_SDS_SD = sds_sensitivity_cycle_SD[c]\n",
    "    _temp_selectivity = selectivity_cycle[c]\n",
    "    _temp_selectivity_SD = selectivity_cycle_SD[c]\n",
    "    _temp_smiles = smiles_cycle[c]\n",
    "    for k in range(c):\n",
    "        _temp = np.append(_temp, sensitivity_cycle[k])\n",
    "        _temp_SD = np.append(_temp_SD, sensitivity_cycle_SD[k])\n",
    "        _temp_SDS = np.append(_temp_SDS, sds_sensitivity_cycle[k])\n",
    "        _temp_SDS_SD = np.append(_temp_SDS_SD, sds_sensitivity_cycle_SD[k])\n",
    "        _temp_selectivity = np.append(_temp_selectivity, selectivity_cycle[k])\n",
    "        _temp_selectivity_SD = np.append(_temp_selectivity_SD, selectivity_cycle_SD[k])\n",
    "        _temp_smiles = np.append(_temp_smiles, smiles_cycle[k])\n",
    "        \n",
    "        #catenated_sensitivity.append(sensitivity_cycle[k])\n",
    "        #print(c,k)\n",
    "     \n",
    "    catenated_sensitivity.append(_temp)\n",
    "    catenated_sensitivity_SD.append(_temp_SD)\n",
    "    catenated_SDS_sensitivity.append(_temp_SDS)\n",
    "    catenated_SDS_sensitivity_SD.append(_temp_SDS_SD)\n",
    "    catenated_selectivity.append(_temp_selectivity)\n",
    "    catenated_selectivity_SD.append(_temp_selectivity_SD)\n",
    "    catenated_smiles.append(_temp_smiles)\n",
    "\n",
    "\n",
    "kb_sensitivity_cycle = []\n",
    "kb_sensitivity_cycle_SD = []\n",
    "sds_kb_sensitivity_cycle = []\n",
    "sds_kb_sensitivity_cycle_SD = []\n",
    "kb_selectivity_cycle = []\n",
    "kb_selectivity_cycle_SD = []\n",
    "\n",
    "kT = 1 #300*0.008314\n",
    "for c in range(cycles):\n",
    "\n",
    "    kb_sensitivity_cycle.append(np.array(new_Kb_data_cycle_PFOS[c]).flatten())\n",
    "    kb_sensitivity_cycle_SD.append(np.array(new_Kb_data_cycle_PFOS_SD[c]).flatten())\n",
    "    \n",
    "    sds_kb_sensitivity_cycle.append(np.array(new_Kb_data_cycle_SDS[c]).flatten())\n",
    "    sds_kb_sensitivity_cycle_SD.append(np.array(new_Kb_data_cycle_SDS_SD[c]).flatten())\n",
    "    \n",
    "    kb_selectivity = np.array(new_Kb_data_cycle_PFOS[c]).flatten()/np.array(new_Kb_data_cycle_SDS[c]).flatten()\n",
    "    \n",
    "    delta_x = new_Kb_data_cycle_PFOS_SD[c]\n",
    "    x = new_Kb_data_cycle_PFOS[c]\n",
    "    delta_y = new_Kb_data_cycle_SDS_SD[c]\n",
    "    y = new_Kb_data_cycle_SDS[c]\n",
    "    kb_selectivityError = kb_selectivity * np.sqrt((delta_x/x)**2 + (delta_y/y)**2)\n",
    "    \n",
    "    kb_selectivity_cycle.append(kb_selectivity)\n",
    "    kb_selectivity_cycle_SD.append(kb_selectivityError)\n",
    "    \n",
    "\n",
    "catenated_kb_sensitivity = []\n",
    "catenated_kb_sensitivity_SD = []\n",
    "catenated_kb_sensitivity_SDS = []\n",
    "catenated_kb_sensitivity_SDS_SD = []\n",
    "catenated_kb_selectivity = []\n",
    "catenated_kb_selectivity_SD = []\n",
    "for c in range(cycles):\n",
    "    \n",
    "    _kb_temp = kb_sensitivity_cycle[c]\n",
    "    _kb_temp_SD = kb_sensitivity_cycle_SD[c]\n",
    "    _kb_temp_SDS = sds_kb_sensitivity_cycle[c]\n",
    "    _kb_temp_SDS_SD = sds_kb_sensitivity_cycle_SD[c]\n",
    "    _kb_temp_selectivity = kb_selectivity_cycle[c]\n",
    "    _kb_temp_selectivity_SD = kb_selectivity_cycle_SD[c]\n",
    "    for k in range(c):\n",
    "        _kb_temp = np.append(_kb_temp, kb_sensitivity_cycle[k])\n",
    "        _kb_temp_SD = np.append(_kb_temp_SD, kb_sensitivity_cycle_SD[k])\n",
    "        _kb_temp_SDS = np.append(_kb_temp_SDS, sds_kb_sensitivity_cycle[k])\n",
    "        _kb_temp_SDS_SD = np.append(_kb_temp_SDS_SD, sds_kb_sensitivity_cycle_SD[k])\n",
    "        _kb_temp_selectivity = np.append(_kb_temp_selectivity, kb_selectivity_cycle[k])\n",
    "        _kb_temp_selectivity_SD = np.append(_kb_temp_selectivity_SD, kb_selectivity_cycle_SD[k])\n",
    "        \n",
    "        #catenated_sensitivity.append(sensitivity_cycle[k])\n",
    "        #print(c,k)\n",
    "     \n",
    "    catenated_kb_sensitivity.append(_kb_temp)\n",
    "    catenated_kb_sensitivity_SD.append(_kb_temp_SD)\n",
    "    catenated_kb_sensitivity_SDS.append(_kb_temp_SDS)\n",
    "    catenated_kb_sensitivity_SDS_SD.append(_kb_temp_SDS_SD)\n",
    "    catenated_kb_selectivity.append(_kb_temp_selectivity)\n",
    "    catenated_kb_selectivity_SD.append(_kb_temp_selectivity_SD)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb3a8930",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.errorbar(catenated_kb_selectivity,catenated_kb_selectivity_SD)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84f2b1d2",
   "metadata": {},
   "source": [
    "#### probes on Pareto frontier in the final round"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c1d96578",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cycle: 0: 2: 0:\n",
      "Pareto: 8 optimal values, 8 objects\n",
      "cycle: 1: 2: 1:\n",
      "cycle: 1\n",
      "Pareto: 7 optimal values, 7 objects\n",
      "cycle: 2: 2: 2:\n",
      "cycle: 2\n",
      "Pareto: 3 optimal values, 3 objects\n",
      "cycle: 3: 2: 3:\n",
      "cycle: 3\n",
      "Pareto: 3 optimal values, 3 objects\n",
      "cycle: 4: 2: 4:\n",
      "cycle: 4\n",
      "Pareto: 3 optimal values, 3 objects\n",
      "cycle: 5: 2: 5:\n",
      "cycle: 5\n",
      "Pareto: 3 optimal values, 3 objects\n",
      "cycle: 6: 3: 0:\n",
      "cycle: 6\n",
      "Pareto: 4 optimal values, 4 objects\n",
      "cycle: 7: 3: 1:\n",
      "cycle: 7\n",
      "Pareto: 4 optimal values, 4 objects\n",
      "cycle: 8: 3: 2:\n",
      "cycle: 8\n",
      "Pareto: 4 optimal values, 4 objects\n",
      "cycle: 9: 3: 3:\n",
      "cycle: 9\n",
      "Pareto: 5 optimal values, 5 objects\n",
      "cycle: 10: 3: 4:\n",
      "cycle: 10\n",
      "Pareto: 5 optimal values, 5 objects\n",
      "cycle:10\n",
      "\n",
      "[93, 100, 147, 182, 251]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 700x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##############################################################################################################################################            \n",
    "\n",
    "labelfont=20\n",
    "tickfont=18\n",
    "legendfont=8\n",
    "\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "fig = plt.figure(figsize=([7,4]))\n",
    "\n",
    "#gs = gridspec.GridSpec(2,6)\n",
    "#gs.update(wspace=1.5, hspace=0.3)\n",
    "\n",
    "fig = plt.figure(figsize=[20,10])\n",
    "fig.tight_layout(pad=100.0)\n",
    "spec = gridspec.GridSpec(ncols=6, nrows=4)\n",
    "fig.subplots_adjust(bottom=0.05, hspace=.4, wspace=.5)\n",
    "\n",
    "ax = ['ax1', 'ax2', 'ax3', 'ax4', 'ax5', 'ax6', 'ax7', 'ax8', 'ax9', 'ax10', 'ax11', 'ax12', 'ax13', 'ax14']\n",
    "\n",
    "\n",
    "kT = 1 #300*0.008314\n",
    "    \n",
    "##############################################################################################################################################            \n",
    "mean_distance_all = []\n",
    "mean_distance_pareto_only = []    \n",
    "##############################################################################################################################################            \n",
    "col=0\n",
    "row=2\n",
    "for c in range(11):\n",
    "    \n",
    "    tickfont=14\n",
    "    \n",
    "    if c!=0:\n",
    "        if c % 6 == 0:\n",
    "            col = 0\n",
    "            row += 1\n",
    "        else:\n",
    "            col += 1\n",
    "    \n",
    "    print(\"cycle: %d: %d: %d:\"% (c, row, col))\n",
    "    \n",
    "    #ax[c] = fig.add_subplot(2, 6, c+3)\n",
    "    pc = c+2\n",
    "    ax[pc] = fig.add_subplot(spec[row, col])\n",
    "    ax[pc].tick_params(axis=\"x\", labelsize=tickfont, direction='in', length=6, width=2, colors='black', rotation=0)\n",
    "    ax[pc].tick_params(axis=\"y\", labelsize=tickfont, direction='in', length=6, width=2, colors='black')\n",
    "    ax[pc].tick_params(which='minor', direction='in', length=3, width=2)\n",
    "    #spec.update(wspace=0.45, hspace=0.7)\n",
    "    \n",
    "    if c == 11:\n",
    "        ax[pc].axis('off')\n",
    "        \n",
    "    \n",
    "    for k in range(c):\n",
    "        ax[pc].scatter(sensitivity_cycle[k], selectivity_cycle[k], \n",
    "                      color=\"gray\", s=25, alpha=0.75, marker='o', label='init')\n",
    "    \n",
    "        ax[pc].errorbar(sensitivity_cycle[k], selectivity_cycle[k], \n",
    "                       xerr= sensitivity_cycle_SD[k], yerr= selectivity_cycle_SD[k], \n",
    "                        color=\"gray\", ms=25, alpha=0.25, elinewidth=2, capsize=2, lw=0)\n",
    "    \n",
    "    ax[pc].scatter(sensitivity_cycle[c], selectivity_cycle[c], \n",
    "                  color=\"darkred\", s=25, alpha=0.75, marker='o', label='init')\n",
    "    \n",
    "    ax[pc].errorbar(sensitivity_cycle[c], selectivity_cycle[c],\n",
    "                   xerr= sensitivity_cycle_SD[c], yerr= selectivity_cycle_SD[c], \n",
    "                    color=\"darkred\", ms=25, alpha=0.25, elinewidth=2, capsize=2, lw=0)\n",
    "\n",
    "    pareto=oapackage.ParetoDoubleLong()\n",
    "    for ii in range(0, catenated_sensitivity[0].shape[0]):\n",
    "        \n",
    "        w=oapackage.doubleVector( (catenated_sensitivity[0][ii], catenated_selectivity[0][ii]))\n",
    "        \n",
    "        pareto.addvalue(w, ii)\n",
    "    \n",
    "    lst_init=pareto.allindices() # the indices of the Pareto optimal designs\n",
    "\n",
    "    init_Pareto_sensitivity = catenated_sensitivity[0][list(lst_init)]\n",
    "    init_Pareto_selectivity = catenated_selectivity[0][list(lst_init)]\n",
    "    arg_sort_list = np.argsort(init_Pareto_sensitivity)\n",
    "    init_Pareto_sensitivity = init_Pareto_sensitivity[arg_sort_list]\n",
    "    init_Pareto_selectivity = init_Pareto_selectivity[arg_sort_list]\n",
    "\n",
    "    \n",
    "    \n",
    "    if c == 0:\n",
    "        \n",
    "        sensitivity = catenated_sensitivity[0]\n",
    "        selectivity = catenated_selectivity[0]\n",
    "        \n",
    "        \n",
    "        Pareto_sensitivity = init_Pareto_sensitivity\n",
    "        Pareto_selectivity = init_Pareto_selectivity\n",
    "        \n",
    "        pareto.show(verbose=1)\n",
    "        \n",
    "    else:\n",
    "        # current\n",
    "        print(\"cycle: %d\" % c)\n",
    "        \n",
    "        sensitivity = catenated_sensitivity[c]\n",
    "        selectivity = catenated_selectivity[c]\n",
    "        \n",
    "        pareto=oapackage.ParetoDoubleLong()\n",
    "        for ii in range(0, catenated_sensitivity[c].shape[0]):\n",
    "            \n",
    "            w=oapackage.doubleVector( (catenated_sensitivity[c][ii],catenated_selectivity[c][ii]) )\n",
    "            \n",
    "            pareto.addvalue(w, ii)\n",
    "\n",
    "        pareto.show(verbose=1)\n",
    "\n",
    "        lst=pareto.allindices() # the indices of the Pareto optimal designs\n",
    "        \n",
    "        Pareto_sensitivity = catenated_sensitivity[c][list(lst)]\n",
    "        Pareto_selectivity = catenated_selectivity[c][list(lst)]\n",
    "        arg_sort_list = np.argsort(Pareto_sensitivity)[::-1] # reverse for proper green shade fill\n",
    "        Pareto_sensitivity = Pareto_sensitivity[arg_sort_list]\n",
    "        Pareto_selectivity = Pareto_selectivity[arg_sort_list]\n",
    "        \n",
    "        \n",
    "        ax[pc].fill(np.append(init_Pareto_sensitivity,Pareto_sensitivity), \n",
    "                   np.append(init_Pareto_selectivity,Pareto_selectivity), 'mediumseagreen', alpha=0.5)\n",
    "        \n",
    "        if c==10:\n",
    "            print(f\"cycle:{c}\\n\")\n",
    "            final_round_Pareto_catenated_indices = list(lst)\n",
    "            print(final_round_Pareto_catenated_indices)\n",
    "\n",
    "    \n",
    "    ax[pc].plot(Pareto_sensitivity,Pareto_selectivity, color=\"black\", alpha=1, ms=0, marker='o', lw=3)\n",
    "    \n",
    "    if c==10:\n",
    "        colors=['blue', 'green', 'orange', 'red', 'black']\n",
    "        for m in range(Pareto_sensitivity.shape[0]):\n",
    "            ax[pc].plot(Pareto_sensitivity[m],Pareto_selectivity[m], c=colors[m], alpha=1, ms=5, marker='o', lw=3)\n",
    "        \n",
    "    #ax[pc].scatter(Pareto_sensitivity,Pareto_selectivity, color=\"black\", alpha=1, s=20, marker='o', lw=3)\n",
    "    \n",
    "\n",
    "\n",
    "    lkT=300*0.008314\n",
    "    ax[pc].set_xlim(-1*lkT, 4.25*lkT)\n",
    "    ax[pc].set_ylim(-4.5*lkT, 2.5*lkT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b343bca1",
   "metadata": {},
   "source": [
    "#### final round Pareto frontier deltaG and Kb based sensitivity and selectivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "09e0596e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deltaG sensitivity is [7.957 6.851 9.484 9.795 9.312]+/-[1.689 0.751 0.733 0.214 1.373]\n",
      "deltaG sensitivity is [8.  6.9 9.5 9.8 9.3]+/-[1.7 0.8 0.7 0.2 1.4]\n",
      "\n",
      "\n",
      "SDS deltaG sensitivity is [ 6.179  3.776  9.478 15.442  9.224]+/-[0.945 1.309 0.018 0.704 0.362]\n",
      "SDS deltaG sensitivity is [ 6.2  3.8  9.5 15.4  9.2]+/-[0.9 1.3 0.  0.7 0.4]\n",
      "\n",
      "\n",
      "deltaG selectivity is [ 1.778  3.076  0.006 -5.647  0.088]+/-[1.935 1.509 0.734 0.736 1.42 ]\n",
      "deltaG selectivity is [ 1.8  3.1  0.  -5.6  0.1]+/-[1.9 1.5 0.7 0.7 1.4]\n",
      "\n",
      "\n",
      "Kb sensitivity is [ 98.967  62.683 118.349 177.355 129.738]+/-[67.598 15.287 20.752 12.716 45.597]\n",
      "Kb sensitivity is [ 99.   62.7 118.3 177.4 129.7]+/-[67.6 15.3 20.8 12.7 45.6]\n",
      "\n",
      "\n",
      "SDS Kb sensitivity is [  47.895   13.637  106.19  1119.916  118.498]+/-[ 14.664   3.819   1.988 310.229  16.061]\n",
      "SDS Kb sensitivity is [  47.9   13.6  106.2 1119.9  118.5]+/-[ 14.7   3.8   2.  310.2  16.1]\n",
      "\n",
      "\n",
      "Kb selectivity is [2.066 4.597 1.114 0.158 1.095]+/-[1.547 1.707 0.197 0.045 0.412]\n",
      "Kb selectivity is [2.1 4.6 1.1 0.2 1.1]+/-[1.5 1.7 0.2 0.  0.4]\n",
      "\n",
      "\n",
      "SMILES is ['NC(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)Cl'\n",
      " 'CCCCCC(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)N(C)C'\n",
      " 'CN(C)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)Cl'\n",
      " 'CP(C)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)Br'\n",
      " 'CN(C)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(C)(Cl)Cl']\n"
     ]
    }
   ],
   "source": [
    "final_round_Pareto_catenated_indices\n",
    "print(f\"deltaG sensitivity is {np.round(catenated_sensitivity[10][final_round_Pareto_catenated_indices],3)}+/-{np.round(catenated_sensitivity_SD[10][final_round_Pareto_catenated_indices],3)}\")\n",
    "print(f\"deltaG sensitivity is {np.round(catenated_sensitivity[10][final_round_Pareto_catenated_indices],1)}+/-{np.round(catenated_sensitivity_SD[10][final_round_Pareto_catenated_indices],1)}\")\n",
    "\n",
    "print(\"\\n\")\n",
    "\n",
    "print(f\"SDS deltaG sensitivity is {np.round(catenated_SDS_sensitivity[10][final_round_Pareto_catenated_indices],3)}+/-{np.round(catenated_SDS_sensitivity_SD[10][final_round_Pareto_catenated_indices],3)}\")\n",
    "print(f\"SDS deltaG sensitivity is {np.round(catenated_SDS_sensitivity[10][final_round_Pareto_catenated_indices],1)}+/-{np.round(catenated_SDS_sensitivity_SD[10][final_round_Pareto_catenated_indices],1)}\")\n",
    "\n",
    "print(\"\\n\")\n",
    "\n",
    "print(f\"deltaG selectivity is {np.round(catenated_selectivity[10][final_round_Pareto_catenated_indices],3)}+/-{np.round(catenated_selectivity_SD[10][final_round_Pareto_catenated_indices],3)}\")\n",
    "print(f\"deltaG selectivity is {np.round(catenated_selectivity[10][final_round_Pareto_catenated_indices],1)}+/-{np.round(catenated_selectivity_SD[10][final_round_Pareto_catenated_indices],1)}\")\n",
    "\n",
    "print(\"\\n\")\n",
    "\n",
    "print(f\"Kb sensitivity is {np.round(catenated_kb_sensitivity[10][final_round_Pareto_catenated_indices],3)}+/-{np.round(catenated_kb_sensitivity_SD[10][final_round_Pareto_catenated_indices],3)}\")\n",
    "print(f\"Kb sensitivity is {np.round(catenated_kb_sensitivity[10][final_round_Pareto_catenated_indices],1)}+/-{np.round(catenated_kb_sensitivity_SD[10][final_round_Pareto_catenated_indices],1)}\")\n",
    "\n",
    "print(\"\\n\")\n",
    "\n",
    "print(f\"SDS Kb sensitivity is {np.round(catenated_kb_sensitivity_SDS[10][final_round_Pareto_catenated_indices],3)}+/-{np.round(catenated_kb_sensitivity_SDS_SD[10][final_round_Pareto_catenated_indices],3)}\")\n",
    "print(f\"SDS Kb sensitivity is {np.round(catenated_kb_sensitivity_SDS[10][final_round_Pareto_catenated_indices],1)}+/-{np.round(catenated_kb_sensitivity_SDS_SD[10][final_round_Pareto_catenated_indices],1)}\")\n",
    "\n",
    "print(\"\\n\")\n",
    "\n",
    "print(f\"Kb selectivity is {np.round(catenated_kb_selectivity[10][final_round_Pareto_catenated_indices],3)}+/-{np.round(catenated_kb_selectivity_SD[10][final_round_Pareto_catenated_indices],3)}\")\n",
    "print(f\"Kb selectivity is {np.round(catenated_kb_selectivity[10][final_round_Pareto_catenated_indices],1)}+/-{np.round(catenated_kb_selectivity_SD[10][final_round_Pareto_catenated_indices],1)}\")\n",
    "\n",
    "\n",
    "print(\"\\n\")\n",
    "\n",
    "print(f\"SMILES is {catenated_smiles[10][final_round_Pareto_catenated_indices]}\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c609f29",
   "metadata": {},
   "source": [
    "#### create a pandas dataframe and a latex table --- SI tables in LaTeX format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a35b4e9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(45, 7) (45, 39)\n",
      "(13, 7) (13, 39)\n",
      "(27, 7) (27, 39)\n",
      "(29, 7) (29, 39)\n",
      "(19, 7) (19, 39)\n",
      "(18, 7) (18, 39)\n",
      "(18, 7) (18, 39)\n",
      "(19, 7) (19, 39)\n",
      "(24, 7) (24, 39)\n",
      "(20, 7) (20, 39)\n",
      "(20, 7) (20, 39)\n"
     ]
    }
   ],
   "source": [
    "simdir='../Data'\n",
    "\n",
    "each_cycle_data = []\n",
    "for c in range(11):\n",
    "    \n",
    "    #each_cycle_data[c].to_csv(simdir+'/analysis/NB_PFAS/MS_Figs/data-each-cycle-JCED/mw_cas_iupac/cycle-'+str(c)+'.txt',sep='\\t')\n",
    "    each_cycle_data.append(pd.read_csv(simdir+'/mw_cas_iupac_format/cycle-'+str(c)+'.txt',sep='\\t'))\n",
    "  \n",
    "\n",
    "\n",
    "each_cycle_data = []\n",
    "\n",
    "for c in range(11):\n",
    "    each_cycle_data.append(pd.read_csv(simdir+'/mw_cas_iupac/cycle-'+str(c)+'.txt',sep='\\t'))\n",
    "    \n",
    "    nwaters = pd.read_csv(simdir+'/folder-pdb-each-cycle/jcycle-water-'+str(c)+'.txt',sep='\\t', header=None,\n",
    "                          names=['index', 'pdb', 'smiles', 'PFOS_nwater', 'SDS_nwater', 'PFOS_nbox', 'SDS_nbox'] )\n",
    "    \n",
    "    \n",
    "    #print(nwaters)\n",
    "    print(nwaters.shape, each_cycle_data[c].shape)\n",
    "    _nwaters = []\n",
    "    _box = []\n",
    "    _structs = []\n",
    "    _smiles = []\n",
    "    _iupac = []\n",
    "    _mw = []\n",
    "    _sds_sensitivity = []\n",
    "    _kb_sds_sensitivity = []\n",
    "    _smiles_string = []\n",
    "    for j in range(nwaters.shape[0]):\n",
    "        _nwaters.append( str(nwaters.iloc[j]['PFOS_nwater']) + ',' + str(nwaters.iloc[j]['SDS_nwater']) )\n",
    "        _box.append( str(nwaters.iloc[j]['PFOS_nbox']) + ',' + str(nwaters.iloc[j]['SDS_nbox']) )\n",
    "        \n",
    "        __struct = '\\\\' + 'begin{minipage}[t]{1\\\\'+ 'linewidth} ' \n",
    "        __struct += '\\includegraphics[angle=-45,width=\\\\textwidth]{./data-each-cycle-JCED/smiles-each-cycle/cycle_'+str(c)+'/struct_'+str(j)+'.png}'\n",
    "        __struct += ' \\\\end{minipage}' \n",
    "    \n",
    "        _structs.append(__struct)\n",
    "        \n",
    "        #_smiles.append('\\seqsplit{' + each_cycle_data[c].iloc[j]['Probe SMILES'] + '}')\n",
    "        _smiles.append('\\seqsplit{' + nwaters.iloc[j]['smiles'] + '}')\n",
    "        #_smiles.append( nwaters.iloc[j]['smiles'] )\n",
    "        \n",
    "        _iupac.append('\\seqsplit{' + each_cycle_data[c].iloc[j]['IUPAC'] + '}')\n",
    "        \n",
    "        _mw.append(str(np.round(each_cycle_data[c].iloc[j]['Molecular weight [kg/kmol]'], 2)))\n",
    "        \n",
    "        _sds_sensitivity.append( str(np.round(sds_sensitivity_cycle[c][j], 1)) + '$\\\\pm$' +  str(np.round(sds_sensitivity_cycle_SD[c][j], 1)) )\n",
    "\n",
    "        _kb_sds_sensitivity.append( str(np.round(sds_kb_sensitivity_cycle[c][j], 1)) + '$\\\\pm$' +  str(np.round(sds_kb_sensitivity_cycle_SD[c][j], 1)) )\n",
    "        \n",
    "        _smiles_string.append(smiles_cycle[c][j])\n",
    "        \n",
    "        \n",
    "    each_cycle_data[c]['Structure'] = np.array(_structs)\n",
    "    each_cycle_data[c]['Probe SMILES'] = np.array(_smiles)\n",
    "    each_cycle_data[c]['IUPAC'] = np.array(_iupac)\n",
    "    each_cycle_data[c]['Molecular weight [kg/kmol]'] = np.array(_mw)\n",
    "    \n",
    "    each_cycle_data[c]['$\\\\Delta G_{SDS}$ [kJ/mol]'] = _sds_sensitivity\n",
    "    each_cycle_data[c]['$K_b^\\mathrm{SDS}$'] = _kb_sds_sensitivity\n",
    "    \n",
    "    each_cycle_data[c]['smiles_string'] = _smiles_string\n",
    "    \n",
    "\n",
    "    #print(c)\n",
    "    #print(_nwaters)\n",
    "    #print(each_cycle_data[c])\n",
    "    each_cycle_data[c]['Num. of Water (PFOS, SDS)'] = _nwaters \n",
    "    each_cycle_data[c]['Box length [1e-9 m] (PFOS, SDS)'] = _box\n",
    "    #each_cycle_data[c]['Molecular weight [kg/kmol]'] = np.round(each_cycle_data[c]['Molecular weight [kg/kmol]'], 2)\n",
    "    \n",
    "    \n",
    "    each_cycle_data[c].rename(columns={'Probe SMILES': 'SMILES'}, inplace=True)\n",
    "    each_cycle_data[c].rename(columns={'Molecular weight [kg/kmol]': 'Mol. weight [kg/kmol]'}, inplace=True)\n",
    "    each_cycle_data[c].rename(columns={'Num. of Water (PFOS, SDS)': '\\# Water molecules (PFOS, SDS)'}, inplace=True)\n",
    "    \n",
    "    #print(each_cycle_data[c])\n",
    "    \n",
    "    \n",
    "chem_sample_table = []\n",
    "#for c in range(11):\n",
    "#    chem_sample_table.append(each_cycle_data[c][['Probe SMILES', 'Molecular weight [kg/kmol]', 'Structure', \n",
    "#                                                 'Avg. Sensitivity (-$\\\\Delta G_{PFOS}$) [kJ/mol]', \n",
    "#                                                 'S.E. Sensitivity (-$\\\\Delta G_{PFOS}$) [kJ/mol]',\n",
    "#                                                 'Avg. Selectivity (-$\\\\Delta G_{PFOS-SDS}$) [kJ/mol]', \n",
    "#                                                 'S.E. Selectivity (-$\\\\Delta G_{PFOS-SDS}$) [kJ/mol]']])\n",
    "    \n",
    "for c in range(11):\n",
    "    each_cycle_data[c].rename(columns={'Selectivity (-$\\\\Delta G_{PFOS-SDS}$) [kJ/mol]': 'Selectivity, $-\\\\Delta\\\\Delta G_{PFOS - SDS}$ [kJ/mol]'}, inplace=True)\n",
    "    each_cycle_data[c].rename(columns={'Sensitivity (-$\\\\Delta G_{PFOS}$) [kJ/mol]': 'Sensitivity, $-\\\\Delta G_{PFOS}$ [kJ/mol]'}, inplace=True)\n",
    "\n",
    "    chem_sample_table.append(each_cycle_data[c][['\\#', 'SMILES', 'CAS RN', 'IUPAC',\n",
    "                                                 'Mol. weight [kg/kmol]', 'Structure', '\\# Water molecules (PFOS, SDS)',\n",
    "                                                 'Box length [1e-9 m] (PFOS, SDS)',\n",
    "                                                 'Sensitivity, $-\\\\Delta G_{PFOS}$ [kJ/mol]',\n",
    "                                                 '$\\\\Delta G_{SDS}$ [kJ/mol]',\n",
    "                                                 'Selectivity, $-\\\\Delta\\\\Delta G_{PFOS - SDS}$ [kJ/mol]',\n",
    "                                                 '$K_b^\\mathrm{PFOS}$',\n",
    "                                                 '$K_b^\\mathrm{SDS}$',\n",
    "                                                 '$K_b^\\mathrm{PFOS}/K_b^\\mathrm{SDS}$',\n",
    "                                                 'smiles_string']])\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3f1cb265",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>\\#</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>CAS RN</th>\n",
       "      <th>IUPAC</th>\n",
       "      <th>Mol. weight [kg/kmol]</th>\n",
       "      <th>Structure</th>\n",
       "      <th>\\# Water molecules (PFOS, SDS)</th>\n",
       "      <th>Box length [1e-9 m] (PFOS, SDS)</th>\n",
       "      <th>Sensitivity, $-\\Delta G_{PFOS}$ [kJ/mol]</th>\n",
       "      <th>$\\Delta G_{SDS}$ [kJ/mol]</th>\n",
       "      <th>Selectivity, $-\\Delta\\Delta G_{PFOS - SDS}$ [kJ/mol]</th>\n",
       "      <th>$K_b^\\mathrm{PFOS}$</th>\n",
       "      <th>$K_b^\\mathrm{SDS}$</th>\n",
       "      <th>$K_b^\\mathrm{PFOS}/K_b^\\mathrm{SDS}$</th>\n",
       "      <th>smiles_string</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>\\seqsplit{CCCC}</td>\n",
       "      <td>106-97-8</td>\n",
       "      <td>\\seqsplit{butane}</td>\n",
       "      <td>58.08</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>5301,5300</td>\n",
       "      <td>5.5,5.5</td>\n",
       "      <td>0.5$\\pm$0.5</td>\n",
       "      <td>0.6$\\pm$0.1</td>\n",
       "      <td>-0.1$\\pm$0.5</td>\n",
       "      <td>4.1$\\pm$0.7</td>\n",
       "      <td>3.7$\\pm$0.2</td>\n",
       "      <td>1.1$\\pm$0.2</td>\n",
       "      <td>CCCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>\\seqsplit{CCCN}</td>\n",
       "      <td>68187-45-1</td>\n",
       "      <td>\\seqsplit{propan-1-amine}</td>\n",
       "      <td>59.07</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>7008,7008</td>\n",
       "      <td>6.0,6.0</td>\n",
       "      <td>1.3$\\pm$0.9</td>\n",
       "      <td>-0.3$\\pm$0.1</td>\n",
       "      <td>1.5$\\pm$0.9</td>\n",
       "      <td>5.1$\\pm$0.9</td>\n",
       "      <td>2.7$\\pm$0.1</td>\n",
       "      <td>1.9$\\pm$0.3</td>\n",
       "      <td>CCCN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>\\seqsplit{CCCCCCCC}</td>\n",
       "      <td>31372-91-5</td>\n",
       "      <td>\\seqsplit{octane}</td>\n",
       "      <td>114.14</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>5297,5296</td>\n",
       "      <td>5.5,5.5</td>\n",
       "      <td>2.6$\\pm$0.3</td>\n",
       "      <td>3.8$\\pm$0.1</td>\n",
       "      <td>-1.2$\\pm$0.3</td>\n",
       "      <td>8.8$\\pm$0.7</td>\n",
       "      <td>11.3$\\pm$0.7</td>\n",
       "      <td>0.8$\\pm$0.1</td>\n",
       "      <td>CCCCCCCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>\\seqsplit{CCCCCC}</td>\n",
       "      <td>92112-69-1</td>\n",
       "      <td>\\seqsplit{hexane}</td>\n",
       "      <td>86.11</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>5297,5296</td>\n",
       "      <td>5.5,5.5</td>\n",
       "      <td>1.8$\\pm$0.6</td>\n",
       "      <td>1.8$\\pm$0.2</td>\n",
       "      <td>0.0$\\pm$0.7</td>\n",
       "      <td>6.6$\\pm$1.3</td>\n",
       "      <td>5.4$\\pm$0.3</td>\n",
       "      <td>1.2$\\pm$0.3</td>\n",
       "      <td>CCCCCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>\\seqsplit{CCCNC}</td>\n",
       "      <td>627-35-0</td>\n",
       "      <td>\\seqsplit{N-methylpropan-1-amine}</td>\n",
       "      <td>73.09</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>7006,7006</td>\n",
       "      <td>6.0,6.0</td>\n",
       "      <td>-0.9$\\pm$0.3</td>\n",
       "      <td>0.1$\\pm$0.3</td>\n",
       "      <td>-1.0$\\pm$0.4</td>\n",
       "      <td>2.6$\\pm$0.2</td>\n",
       "      <td>3.1$\\pm$0.3</td>\n",
       "      <td>0.8$\\pm$0.1</td>\n",
       "      <td>CCCNC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>\\seqsplit{CCCCCCCCCC}</td>\n",
       "      <td>63335-87-5</td>\n",
       "      <td>\\seqsplit{decane}</td>\n",
       "      <td>142.17</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>7000,7000</td>\n",
       "      <td>6.0,6.0</td>\n",
       "      <td>3.9$\\pm$0.2</td>\n",
       "      <td>4.4$\\pm$0.1</td>\n",
       "      <td>-0.6$\\pm$0.2</td>\n",
       "      <td>14.0$\\pm$0.8</td>\n",
       "      <td>12.2$\\pm$0.3</td>\n",
       "      <td>1.1$\\pm$0.1</td>\n",
       "      <td>CCCCCCCCCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>\\seqsplit{CCCC(Cl)(Cl)Cl}</td>\n",
       "      <td>13279-85-1</td>\n",
       "      <td>\\seqsplit{1,1,1-trichlorobutane}</td>\n",
       "      <td>159.96</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>7004,7004</td>\n",
       "      <td>6.0,6.0</td>\n",
       "      <td>2.2$\\pm$0.5</td>\n",
       "      <td>2.5$\\pm$0.2</td>\n",
       "      <td>-0.2$\\pm$0.6</td>\n",
       "      <td>8.1$\\pm$1.2</td>\n",
       "      <td>7.5$\\pm$0.8</td>\n",
       "      <td>1.1$\\pm$0.2</td>\n",
       "      <td>CCCC(Cl)(Cl)Cl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>\\seqsplit{CCCC(F)(F)F}</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,1-trifluorobutane}</td>\n",
       "      <td>112.05</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>5299,5298</td>\n",
       "      <td>5.5,5.5</td>\n",
       "      <td>2.1$\\pm$0.3</td>\n",
       "      <td>1.2$\\pm$0.2</td>\n",
       "      <td>0.9$\\pm$0.4</td>\n",
       "      <td>7.4$\\pm$0.8</td>\n",
       "      <td>4.6$\\pm$0.3</td>\n",
       "      <td>1.6$\\pm$0.2</td>\n",
       "      <td>CCCC(F)(F)F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>\\seqsplit{NCCC(F)(F)F}</td>\n",
       "      <td>460-39-9</td>\n",
       "      <td>\\seqsplit{3,3,3-trifluoropropan-1-amine}</td>\n",
       "      <td>113.05</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>7007,7007</td>\n",
       "      <td>6.0,6.0</td>\n",
       "      <td>0.4$\\pm$0.4</td>\n",
       "      <td>0.7$\\pm$0.2</td>\n",
       "      <td>-0.3$\\pm$0.5</td>\n",
       "      <td>4.0$\\pm$0.4</td>\n",
       "      <td>3.8$\\pm$0.2</td>\n",
       "      <td>1.0$\\pm$0.1</td>\n",
       "      <td>NCCC(F)(F)F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>\\seqsplit{CCC[N+](C)(C)C}</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{trimethyl(propyl)azanium}</td>\n",
       "      <td>102.13</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>6999,6999</td>\n",
       "      <td>6.0,6.0</td>\n",
       "      <td>1.0$\\pm$0.5</td>\n",
       "      <td>2.2$\\pm$0.3</td>\n",
       "      <td>-1.2$\\pm$0.6</td>\n",
       "      <td>5.8$\\pm$0.8</td>\n",
       "      <td>6.8$\\pm$0.6</td>\n",
       "      <td>0.9$\\pm$0.1</td>\n",
       "      <td>CCC[N+](C)(C)C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>\\seqsplit{CCCCCC(F)(F)F}</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,1-trifluorohexane}</td>\n",
       "      <td>140.08</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>5297,5296</td>\n",
       "      <td>5.5,5.5</td>\n",
       "      <td>2.1$\\pm$0.8</td>\n",
       "      <td>2.9$\\pm$0.1</td>\n",
       "      <td>-0.9$\\pm$0.8</td>\n",
       "      <td>7.2$\\pm$1.8</td>\n",
       "      <td>7.9$\\pm$0.3</td>\n",
       "      <td>0.9$\\pm$0.2</td>\n",
       "      <td>CCCCCC(F)(F)F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>\\seqsplit{CCCCCCCCCCCC}</td>\n",
       "      <td>112-40-3</td>\n",
       "      <td>\\seqsplit{dodecane}</td>\n",
       "      <td>170.2</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>11197,11197</td>\n",
       "      <td>7.0,7.0</td>\n",
       "      <td>4.3$\\pm$0.6</td>\n",
       "      <td>6.0$\\pm$0.3</td>\n",
       "      <td>-1.7$\\pm$0.7</td>\n",
       "      <td>13.2$\\pm$1.9</td>\n",
       "      <td>18.6$\\pm$1.7</td>\n",
       "      <td>0.7$\\pm$0.1</td>\n",
       "      <td>CCCCCCCCCCCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>\\seqsplit{CNCCC(F)(F)F}</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{3,3,3-trifluoro-N-methylpropan-1-amine}</td>\n",
       "      <td>127.06</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>7008,7008</td>\n",
       "      <td>6.0,6.0</td>\n",
       "      <td>1.7$\\pm$0.3</td>\n",
       "      <td>1.6$\\pm$0.2</td>\n",
       "      <td>0.0$\\pm$0.4</td>\n",
       "      <td>6.3$\\pm$0.5</td>\n",
       "      <td>4.9$\\pm$0.3</td>\n",
       "      <td>1.3$\\pm$0.1</td>\n",
       "      <td>CNCCC(F)(F)F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>\\seqsplit{CN(C)CCC(Cl)(Cl)Cl}</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{3,3,3-trichloro-N,N-dimethylpropan-1...</td>\n",
       "      <td>188.99</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>7002,7002</td>\n",
       "      <td>6.0,6.0</td>\n",
       "      <td>3.2$\\pm$0.4</td>\n",
       "      <td>2.5$\\pm$0.6</td>\n",
       "      <td>0.6$\\pm$0.7</td>\n",
       "      <td>11.5$\\pm$1.6</td>\n",
       "      <td>7.1$\\pm$1.4</td>\n",
       "      <td>1.6$\\pm$0.4</td>\n",
       "      <td>CN(C)CCC(Cl)(Cl)Cl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>\\seqsplit{CCCCCCCC(F)(F)F}</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,1-trifluorooctane}</td>\n",
       "      <td>168.11</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>5297,5296</td>\n",
       "      <td>5.5,5.5</td>\n",
       "      <td>2.4$\\pm$0.2</td>\n",
       "      <td>3.5$\\pm$0.0</td>\n",
       "      <td>-1.1$\\pm$0.2</td>\n",
       "      <td>7.8$\\pm$0.6</td>\n",
       "      <td>10.3$\\pm$0.2</td>\n",
       "      <td>0.8$\\pm$0.1</td>\n",
       "      <td>CCCCCCCC(F)(F)F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>\\seqsplit{CCCCCCCCCC(F)(F)F}</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,1-trifluorodecane}</td>\n",
       "      <td>196.14</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>7001,7001</td>\n",
       "      <td>6.0,6.0</td>\n",
       "      <td>3.7$\\pm$0.1</td>\n",
       "      <td>5.2$\\pm$0.1</td>\n",
       "      <td>-1.5$\\pm$0.1</td>\n",
       "      <td>12.7$\\pm$0.6</td>\n",
       "      <td>15.6$\\pm$0.6</td>\n",
       "      <td>0.8$\\pm$0.0</td>\n",
       "      <td>CCCCCCCCCC(F)(F)F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>\\seqsplit{CCC(F)(F)C(F)(F)F}</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2-pentafluorobutane}</td>\n",
       "      <td>148.03</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>5298,5297</td>\n",
       "      <td>5.5,5.5</td>\n",
       "      <td>1.5$\\pm$0.3</td>\n",
       "      <td>2.0$\\pm$0.1</td>\n",
       "      <td>-0.4$\\pm$0.4</td>\n",
       "      <td>6.4$\\pm$0.6</td>\n",
       "      <td>6.0$\\pm$0.4</td>\n",
       "      <td>1.1$\\pm$0.1</td>\n",
       "      <td>CCC(F)(F)C(F)(F)F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>\\seqsplit{C[N+](C)(C)CCC(F)(F)F}</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{trimethyl(3,3,3-trifluoropropyl)azan...</td>\n",
       "      <td>156.1</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>7001,7001</td>\n",
       "      <td>6.0,6.0</td>\n",
       "      <td>1.4$\\pm$0.3</td>\n",
       "      <td>1.3$\\pm$0.2</td>\n",
       "      <td>0.0$\\pm$0.4</td>\n",
       "      <td>5.9$\\pm$0.7</td>\n",
       "      <td>5.1$\\pm$0.4</td>\n",
       "      <td>1.1$\\pm$0.2</td>\n",
       "      <td>C[N+](C)(C)CCC(F)(F)F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>\\seqsplit{CCCCCCCCCCCC(F)(F)F}</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,1-trifluorododecane}</td>\n",
       "      <td>224.18</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>11191,11191</td>\n",
       "      <td>7.0,7.0</td>\n",
       "      <td>5.8$\\pm$0.4</td>\n",
       "      <td>8.0$\\pm$0.5</td>\n",
       "      <td>-2.2$\\pm$0.6</td>\n",
       "      <td>21.9$\\pm$2.8</td>\n",
       "      <td>43.2$\\pm$8.6</td>\n",
       "      <td>0.5$\\pm$0.1</td>\n",
       "      <td>CCCCCCCCCCCC(F)(F)F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>\\seqsplit{CC(F)(F)C(F)(F)C(F)(F)F}</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2,3,3-heptafluorobutane}</td>\n",
       "      <td>184.01</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>5298,5297</td>\n",
       "      <td>5.5,5.5</td>\n",
       "      <td>0.6$\\pm$0.4</td>\n",
       "      <td>1.2$\\pm$0.2</td>\n",
       "      <td>-0.7$\\pm$0.4</td>\n",
       "      <td>4.6$\\pm$0.6</td>\n",
       "      <td>4.5$\\pm$0.3</td>\n",
       "      <td>1.0$\\pm$0.1</td>\n",
       "      <td>CC(F)(F)C(F)(F)C(F)(F)F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>\\seqsplit{FC(F)(F)C(F)(F)C(F)(F)C(F)(F)F}</td>\n",
       "      <td>161107-53-5</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2,3,3,4,4,4-decafluorobutane}</td>\n",
       "      <td>237.98</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>5298,5297</td>\n",
       "      <td>5.5,5.5</td>\n",
       "      <td>1.8$\\pm$0.1</td>\n",
       "      <td>2.2$\\pm$0.2</td>\n",
       "      <td>-0.5$\\pm$0.2</td>\n",
       "      <td>7.3$\\pm$0.1</td>\n",
       "      <td>6.9$\\pm$0.6</td>\n",
       "      <td>1.1$\\pm$0.1</td>\n",
       "      <td>FC(F)(F)C(F)(F)C(F)(F)C(F)(F)F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>\\seqsplit{CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)F}</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2,3,3,4,4-nonafluorohexane}</td>\n",
       "      <td>248.02</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>5297,5296</td>\n",
       "      <td>5.5,5.5</td>\n",
       "      <td>1.6$\\pm$0.2</td>\n",
       "      <td>1.4$\\pm$0.3</td>\n",
       "      <td>0.2$\\pm$0.4</td>\n",
       "      <td>7.2$\\pm$0.3</td>\n",
       "      <td>5.1$\\pm$0.6</td>\n",
       "      <td>1.4$\\pm$0.2</td>\n",
       "      <td>CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>\\seqsplit{CC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F}</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2,3,3,4,4,5,5-undecafluorohe...</td>\n",
       "      <td>284.01</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>5296,5295</td>\n",
       "      <td>5.5,5.5</td>\n",
       "      <td>2.1$\\pm$0.2</td>\n",
       "      <td>1.9$\\pm$0.2</td>\n",
       "      <td>0.1$\\pm$0.3</td>\n",
       "      <td>8.3$\\pm$0.7</td>\n",
       "      <td>6.8$\\pm$0.5</td>\n",
       "      <td>1.2$\\pm$0.1</td>\n",
       "      <td>CC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>\\seqsplit{FC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)...</td>\n",
       "      <td>154452-93-4</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,6-tetradec...</td>\n",
       "      <td>337.98</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>5296,5295</td>\n",
       "      <td>5.5,5.5</td>\n",
       "      <td>1.6$\\pm$0.5</td>\n",
       "      <td>1.5$\\pm$0.1</td>\n",
       "      <td>0.1$\\pm$0.5</td>\n",
       "      <td>6.7$\\pm$1.1</td>\n",
       "      <td>5.7$\\pm$0.3</td>\n",
       "      <td>1.2$\\pm$0.2</td>\n",
       "      <td>FC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>\\seqsplit{CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F...</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6-tridecaflu...</td>\n",
       "      <td>348.02</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>5293,5292</td>\n",
       "      <td>5.5,5.5</td>\n",
       "      <td>2.9$\\pm$0.2</td>\n",
       "      <td>4.2$\\pm$0.1</td>\n",
       "      <td>-1.3$\\pm$0.2</td>\n",
       "      <td>10.8$\\pm$1.0</td>\n",
       "      <td>14.3$\\pm$0.9</td>\n",
       "      <td>0.8$\\pm$0.1</td>\n",
       "      <td>CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>\\seqsplit{CC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)...</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7-pentad...</td>\n",
       "      <td>384.0</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>5292,5291</td>\n",
       "      <td>5.5,5.5</td>\n",
       "      <td>2.4$\\pm$0.3</td>\n",
       "      <td>4.2$\\pm$0.4</td>\n",
       "      <td>-1.8$\\pm$0.5</td>\n",
       "      <td>9.6$\\pm$1.0</td>\n",
       "      <td>14.0$\\pm$2.0</td>\n",
       "      <td>0.7$\\pm$0.1</td>\n",
       "      <td>CC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>\\seqsplit{CCCCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F...</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7-pentad...</td>\n",
       "      <td>440.06</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>11193,11193</td>\n",
       "      <td>7.0,7.0</td>\n",
       "      <td>5.0$\\pm$0.4</td>\n",
       "      <td>5.2$\\pm$0.5</td>\n",
       "      <td>-0.2$\\pm$0.6</td>\n",
       "      <td>23.0$\\pm$3.5</td>\n",
       "      <td>21.3$\\pm$4.2</td>\n",
       "      <td>1.1$\\pm$0.3</td>\n",
       "      <td>CCCCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>\\seqsplit{CCCCCC(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl...</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7-pentad...</td>\n",
       "      <td>679.62</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>11185,11185</td>\n",
       "      <td>7.0,7.0</td>\n",
       "      <td>4.9$\\pm$0.2</td>\n",
       "      <td>6.7$\\pm$0.5</td>\n",
       "      <td>-1.7$\\pm$0.6</td>\n",
       "      <td>26.9$\\pm$2.0</td>\n",
       "      <td>45.2$\\pm$9.0</td>\n",
       "      <td>0.6$\\pm$0.1</td>\n",
       "      <td>CCCCCC(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>\\seqsplit{FC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)...</td>\n",
       "      <td>307-34-6</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,8-...</td>\n",
       "      <td>437.97</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>5291,5290</td>\n",
       "      <td>5.5,5.5</td>\n",
       "      <td>3.2$\\pm$0.3</td>\n",
       "      <td>4.6$\\pm$0.2</td>\n",
       "      <td>-1.5$\\pm$0.3</td>\n",
       "      <td>12.2$\\pm$1.2</td>\n",
       "      <td>16.7$\\pm$1.2</td>\n",
       "      <td>0.7$\\pm$0.1</td>\n",
       "      <td>FC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>\\seqsplit{CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F...</td>\n",
       "      <td>85711-89-3</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8-he...</td>\n",
       "      <td>448.01</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>6997,6997</td>\n",
       "      <td>6.0,6.0</td>\n",
       "      <td>3.8$\\pm$0.0</td>\n",
       "      <td>4.8$\\pm$0.1</td>\n",
       "      <td>-1.0$\\pm$0.1</td>\n",
       "      <td>16.4$\\pm$0.4</td>\n",
       "      <td>15.9$\\pm$1.1</td>\n",
       "      <td>1.0$\\pm$0.1</td>\n",
       "      <td>CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>\\seqsplit{CCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(...</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8-hexa...</td>\n",
       "      <td>459.05</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>11191,11191</td>\n",
       "      <td>7.0,7.0</td>\n",
       "      <td>3.5$\\pm$0.2</td>\n",
       "      <td>5.2$\\pm$0.5</td>\n",
       "      <td>-1.7$\\pm$0.5</td>\n",
       "      <td>14.3$\\pm$1.2</td>\n",
       "      <td>16.1$\\pm$2.2</td>\n",
       "      <td>0.9$\\pm$0.1</td>\n",
       "      <td>CCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>\\seqsplit{CCCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)...</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8-he...</td>\n",
       "      <td>476.04</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>11193,11193</td>\n",
       "      <td>7.0,7.0</td>\n",
       "      <td>5.4$\\pm$0.7</td>\n",
       "      <td>5.5$\\pm$0.3</td>\n",
       "      <td>-0.1$\\pm$0.8</td>\n",
       "      <td>27.7$\\pm$6.3</td>\n",
       "      <td>22.9$\\pm$3.1</td>\n",
       "      <td>1.2$\\pm$0.3</td>\n",
       "      <td>CCCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>\\seqsplit{CCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(...</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8-hexa...</td>\n",
       "      <td>473.06</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>11192,11192</td>\n",
       "      <td>7.0,7.0</td>\n",
       "      <td>5.2$\\pm$0.4</td>\n",
       "      <td>6.4$\\pm$0.3</td>\n",
       "      <td>-1.2$\\pm$0.5</td>\n",
       "      <td>27.1$\\pm$2.8</td>\n",
       "      <td>34.5$\\pm$3.2</td>\n",
       "      <td>0.8$\\pm$0.1</td>\n",
       "      <td>CCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>\\seqsplit{CCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(...</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8-hexa...</td>\n",
       "      <td>487.08</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>11190,11190</td>\n",
       "      <td>7.0,7.0</td>\n",
       "      <td>5.2$\\pm$0.4</td>\n",
       "      <td>6.3$\\pm$0.5</td>\n",
       "      <td>-1.1$\\pm$0.6</td>\n",
       "      <td>25.1$\\pm$3.0</td>\n",
       "      <td>28.6$\\pm$5.9</td>\n",
       "      <td>0.9$\\pm$0.2</td>\n",
       "      <td>CCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>\\seqsplit{CC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)...</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,...</td>\n",
       "      <td>483.99</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>6995,6995</td>\n",
       "      <td>6.0,6.0</td>\n",
       "      <td>4.8$\\pm$0.8</td>\n",
       "      <td>5.7$\\pm$0.3</td>\n",
       "      <td>-0.9$\\pm$0.8</td>\n",
       "      <td>23.3$\\pm$6.5</td>\n",
       "      <td>28.6$\\pm$2.6</td>\n",
       "      <td>0.8$\\pm$0.2</td>\n",
       "      <td>CC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>\\seqsplit{CCCC(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(...</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,...</td>\n",
       "      <td>815.46</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>6985,6985</td>\n",
       "      <td>6.0,6.0</td>\n",
       "      <td>7.0$\\pm$0.2</td>\n",
       "      <td>7.9$\\pm$0.1</td>\n",
       "      <td>-0.9$\\pm$0.2</td>\n",
       "      <td>55.6$\\pm$3.3</td>\n",
       "      <td>63.1$\\pm$3.8</td>\n",
       "      <td>0.9$\\pm$0.1</td>\n",
       "      <td>CCCC(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>\\seqsplit{CCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(...</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8-hexa...</td>\n",
       "      <td>502.1</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>11189,11189</td>\n",
       "      <td>7.0,7.0</td>\n",
       "      <td>5.9$\\pm$0.5</td>\n",
       "      <td>6.7$\\pm$0.8</td>\n",
       "      <td>-0.8$\\pm$0.9</td>\n",
       "      <td>33.5$\\pm$4.7</td>\n",
       "      <td>33.9$\\pm$8.5</td>\n",
       "      <td>1.0$\\pm$0.3</td>\n",
       "      <td>CCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>\\seqsplit{CCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(...</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,...</td>\n",
       "      <td>512.02</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>11191,11191</td>\n",
       "      <td>7.0,7.0</td>\n",
       "      <td>5.2$\\pm$0.3</td>\n",
       "      <td>5.4$\\pm$0.8</td>\n",
       "      <td>-0.2$\\pm$0.8</td>\n",
       "      <td>28.9$\\pm$3.7</td>\n",
       "      <td>19.4$\\pm$3.2</td>\n",
       "      <td>1.5$\\pm$0.3</td>\n",
       "      <td>CCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>\\seqsplit{NCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(...</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11...</td>\n",
       "      <td>513.02</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>11191,11191</td>\n",
       "      <td>7.0,7.0</td>\n",
       "      <td>4.5$\\pm$0.8</td>\n",
       "      <td>5.1$\\pm$0.7</td>\n",
       "      <td>-0.7$\\pm$1.0</td>\n",
       "      <td>19.0$\\pm$4.4</td>\n",
       "      <td>19.1$\\pm$4.6</td>\n",
       "      <td>1.0$\\pm$0.3</td>\n",
       "      <td>NCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>\\seqsplit{FC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)...</td>\n",
       "      <td>307-45-9</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,...</td>\n",
       "      <td>537.96</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>6994,6994</td>\n",
       "      <td>6.0,6.0</td>\n",
       "      <td>4.6$\\pm$0.5</td>\n",
       "      <td>5.6$\\pm$0.1</td>\n",
       "      <td>-1.0$\\pm$0.5</td>\n",
       "      <td>22.8$\\pm$4.3</td>\n",
       "      <td>24.5$\\pm$0.7</td>\n",
       "      <td>0.9$\\pm$0.2</td>\n",
       "      <td>FC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>\\seqsplit{CN(C)CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C...</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11...</td>\n",
       "      <td>541.05</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>11189,11189</td>\n",
       "      <td>7.0,7.0</td>\n",
       "      <td>5.1$\\pm$0.2</td>\n",
       "      <td>6.2$\\pm$0.3</td>\n",
       "      <td>-1.1$\\pm$0.3</td>\n",
       "      <td>23.5$\\pm$1.7</td>\n",
       "      <td>30.1$\\pm$2.4</td>\n",
       "      <td>0.8$\\pm$0.1</td>\n",
       "      <td>CN(C)CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>\\seqsplit{CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F...</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,...</td>\n",
       "      <td>548.01</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>11191,11191</td>\n",
       "      <td>7.0,7.0</td>\n",
       "      <td>5.4$\\pm$0.2</td>\n",
       "      <td>5.6$\\pm$0.5</td>\n",
       "      <td>-0.2$\\pm$0.5</td>\n",
       "      <td>29.7$\\pm$3.1</td>\n",
       "      <td>23.2$\\pm$3.7</td>\n",
       "      <td>1.3$\\pm$0.2</td>\n",
       "      <td>CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>\\seqsplit{CC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)...</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,...</td>\n",
       "      <td>583.99</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>11192,11192</td>\n",
       "      <td>7.0,7.0</td>\n",
       "      <td>4.9$\\pm$0.6</td>\n",
       "      <td>6.6$\\pm$0.3</td>\n",
       "      <td>-1.7$\\pm$0.7</td>\n",
       "      <td>22.0$\\pm$4.0</td>\n",
       "      <td>31.3$\\pm$4.3</td>\n",
       "      <td>0.7$\\pm$0.2</td>\n",
       "      <td>CC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>\\seqsplit{FC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)...</td>\n",
       "      <td>307-59-5</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,...</td>\n",
       "      <td>637.96</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>11192,11192</td>\n",
       "      <td>7.0,7.0</td>\n",
       "      <td>5.0$\\pm$0.2</td>\n",
       "      <td>5.5$\\pm$0.2</td>\n",
       "      <td>-0.6$\\pm$0.3</td>\n",
       "      <td>23.0$\\pm$1.9</td>\n",
       "      <td>20.2$\\pm$1.7</td>\n",
       "      <td>1.1$\\pm$0.1</td>\n",
       "      <td>FC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>\\seqsplit{ClC(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(C...</td>\n",
       "      <td>--</td>\n",
       "      <td>\\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,...</td>\n",
       "      <td>1053.19</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>11181,11181</td>\n",
       "      <td>7.0,7.0</td>\n",
       "      <td>8.4$\\pm$0.5</td>\n",
       "      <td>9.3$\\pm$0.8</td>\n",
       "      <td>-1.0$\\pm$0.9</td>\n",
       "      <td>85.8$\\pm$18.6</td>\n",
       "      <td>101.5$\\pm$12.5</td>\n",
       "      <td>0.8$\\pm$0.2</td>\n",
       "      <td>ClC(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    \\#                                             SMILES       CAS RN   \n",
       "0    1                                    \\seqsplit{CCCC}     106-97-8  \\\n",
       "1    2                                    \\seqsplit{CCCN}   68187-45-1   \n",
       "2    3                                \\seqsplit{CCCCCCCC}   31372-91-5   \n",
       "3    4                                  \\seqsplit{CCCCCC}   92112-69-1   \n",
       "4    5                                   \\seqsplit{CCCNC}     627-35-0   \n",
       "5    6                              \\seqsplit{CCCCCCCCCC}   63335-87-5   \n",
       "6    7                          \\seqsplit{CCCC(Cl)(Cl)Cl}   13279-85-1   \n",
       "7    8                             \\seqsplit{CCCC(F)(F)F}           --   \n",
       "8    9                             \\seqsplit{NCCC(F)(F)F}     460-39-9   \n",
       "9   10                          \\seqsplit{CCC[N+](C)(C)C}           --   \n",
       "10  11                           \\seqsplit{CCCCCC(F)(F)F}           --   \n",
       "11  12                            \\seqsplit{CCCCCCCCCCCC}     112-40-3   \n",
       "12  13                            \\seqsplit{CNCCC(F)(F)F}           --   \n",
       "13  14                      \\seqsplit{CN(C)CCC(Cl)(Cl)Cl}           --   \n",
       "14  15                         \\seqsplit{CCCCCCCC(F)(F)F}           --   \n",
       "15  16                       \\seqsplit{CCCCCCCCCC(F)(F)F}           --   \n",
       "16  17                       \\seqsplit{CCC(F)(F)C(F)(F)F}           --   \n",
       "17  18                   \\seqsplit{C[N+](C)(C)CCC(F)(F)F}           --   \n",
       "18  19                     \\seqsplit{CCCCCCCCCCCC(F)(F)F}           --   \n",
       "19  20                 \\seqsplit{CC(F)(F)C(F)(F)C(F)(F)F}           --   \n",
       "20  21          \\seqsplit{FC(F)(F)C(F)(F)C(F)(F)C(F)(F)F}  161107-53-5   \n",
       "21  22         \\seqsplit{CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)F}           --   \n",
       "22  23   \\seqsplit{CC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F}           --   \n",
       "23  24  \\seqsplit{FC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)...  154452-93-4   \n",
       "24  25  \\seqsplit{CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F...           --   \n",
       "25  26  \\seqsplit{CC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)...           --   \n",
       "26  27  \\seqsplit{CCCCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F...           --   \n",
       "27  28  \\seqsplit{CCCCCC(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl...           --   \n",
       "28  29  \\seqsplit{FC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)...     307-34-6   \n",
       "29  30  \\seqsplit{CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F...   85711-89-3   \n",
       "30  31  \\seqsplit{CCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(...           --   \n",
       "31  32  \\seqsplit{CCCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)...           --   \n",
       "32  33  \\seqsplit{CCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(...           --   \n",
       "33  34  \\seqsplit{CCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(...           --   \n",
       "34  35  \\seqsplit{CC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)...           --   \n",
       "35  36  \\seqsplit{CCCC(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(...           --   \n",
       "36  37  \\seqsplit{CCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(...           --   \n",
       "37  38  \\seqsplit{CCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(...           --   \n",
       "38  39  \\seqsplit{NCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(...           --   \n",
       "39  40  \\seqsplit{FC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)...     307-45-9   \n",
       "40  41  \\seqsplit{CN(C)CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C...           --   \n",
       "41  42  \\seqsplit{CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F...           --   \n",
       "42  43  \\seqsplit{CC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)...           --   \n",
       "43  44  \\seqsplit{FC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)...     307-59-5   \n",
       "44  45  \\seqsplit{ClC(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(C...           --   \n",
       "\n",
       "                                                IUPAC Mol. weight [kg/kmol]   \n",
       "0                                   \\seqsplit{butane}                 58.08  \\\n",
       "1                           \\seqsplit{propan-1-amine}                 59.07   \n",
       "2                                   \\seqsplit{octane}                114.14   \n",
       "3                                   \\seqsplit{hexane}                 86.11   \n",
       "4                   \\seqsplit{N-methylpropan-1-amine}                 73.09   \n",
       "5                                   \\seqsplit{decane}                142.17   \n",
       "6                    \\seqsplit{1,1,1-trichlorobutane}                159.96   \n",
       "7                    \\seqsplit{1,1,1-trifluorobutane}                112.05   \n",
       "8            \\seqsplit{3,3,3-trifluoropropan-1-amine}                113.05   \n",
       "9                 \\seqsplit{trimethyl(propyl)azanium}                102.13   \n",
       "10                   \\seqsplit{1,1,1-trifluorohexane}                140.08   \n",
       "11                                \\seqsplit{dodecane}                 170.2   \n",
       "12  \\seqsplit{3,3,3-trifluoro-N-methylpropan-1-amine}                127.06   \n",
       "13  \\seqsplit{3,3,3-trichloro-N,N-dimethylpropan-1...                188.99   \n",
       "14                   \\seqsplit{1,1,1-trifluorooctane}                168.11   \n",
       "15                   \\seqsplit{1,1,1-trifluorodecane}                196.14   \n",
       "16             \\seqsplit{1,1,1,2,2-pentafluorobutane}                148.03   \n",
       "17  \\seqsplit{trimethyl(3,3,3-trifluoropropyl)azan...                 156.1   \n",
       "18                 \\seqsplit{1,1,1-trifluorododecane}                224.18   \n",
       "19         \\seqsplit{1,1,1,2,2,3,3-heptafluorobutane}                184.01   \n",
       "20    \\seqsplit{1,1,1,2,2,3,3,4,4,4-decafluorobutane}                237.98   \n",
       "21      \\seqsplit{1,1,1,2,2,3,3,4,4-nonafluorohexane}                248.02   \n",
       "22  \\seqsplit{1,1,1,2,2,3,3,4,4,5,5-undecafluorohe...                284.01   \n",
       "23  \\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,6-tetradec...                337.98   \n",
       "24  \\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6-tridecaflu...                348.02   \n",
       "25  \\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7-pentad...                 384.0   \n",
       "26  \\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7-pentad...                440.06   \n",
       "27  \\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7-pentad...                679.62   \n",
       "28  \\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,8-...                437.97   \n",
       "29  \\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8-he...                448.01   \n",
       "30  \\seqsplit{1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8-hexa...                459.05   \n",
       "31  \\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8-he...                476.04   \n",
       "32  \\seqsplit{1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8-hexa...                473.06   \n",
       "33  \\seqsplit{1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8-hexa...                487.08   \n",
       "34  \\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,...                483.99   \n",
       "35  \\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,...                815.46   \n",
       "36  \\seqsplit{1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8-hexa...                 502.1   \n",
       "37  \\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,...                512.02   \n",
       "38  \\seqsplit{3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11...                513.02   \n",
       "39  \\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,...                537.96   \n",
       "40  \\seqsplit{3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11...                541.05   \n",
       "41  \\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,...                548.01   \n",
       "42  \\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,...                583.99   \n",
       "43  \\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,...                637.96   \n",
       "44  \\seqsplit{1,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,...               1053.19   \n",
       "\n",
       "                                            Structure   \n",
       "0   \\begin{minipage}[t]{1\\linewidth} \\includegraph...  \\\n",
       "1   \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "2   \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "3   \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "4   \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "5   \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "6   \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "7   \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "8   \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "9   \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "10  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "11  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "12  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "13  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "14  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "15  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "16  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "17  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "18  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "19  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "20  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "21  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "22  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "23  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "24  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "25  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "26  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "27  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "28  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "29  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "30  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "31  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "32  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "33  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "34  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "35  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "36  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "37  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "38  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "39  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "40  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "41  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "42  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "43  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "44  \\begin{minipage}[t]{1\\linewidth} \\includegraph...   \n",
       "\n",
       "   \\# Water molecules (PFOS, SDS) Box length [1e-9 m] (PFOS, SDS)   \n",
       "0                       5301,5300                         5.5,5.5  \\\n",
       "1                       7008,7008                         6.0,6.0   \n",
       "2                       5297,5296                         5.5,5.5   \n",
       "3                       5297,5296                         5.5,5.5   \n",
       "4                       7006,7006                         6.0,6.0   \n",
       "5                       7000,7000                         6.0,6.0   \n",
       "6                       7004,7004                         6.0,6.0   \n",
       "7                       5299,5298                         5.5,5.5   \n",
       "8                       7007,7007                         6.0,6.0   \n",
       "9                       6999,6999                         6.0,6.0   \n",
       "10                      5297,5296                         5.5,5.5   \n",
       "11                    11197,11197                         7.0,7.0   \n",
       "12                      7008,7008                         6.0,6.0   \n",
       "13                      7002,7002                         6.0,6.0   \n",
       "14                      5297,5296                         5.5,5.5   \n",
       "15                      7001,7001                         6.0,6.0   \n",
       "16                      5298,5297                         5.5,5.5   \n",
       "17                      7001,7001                         6.0,6.0   \n",
       "18                    11191,11191                         7.0,7.0   \n",
       "19                      5298,5297                         5.5,5.5   \n",
       "20                      5298,5297                         5.5,5.5   \n",
       "21                      5297,5296                         5.5,5.5   \n",
       "22                      5296,5295                         5.5,5.5   \n",
       "23                      5296,5295                         5.5,5.5   \n",
       "24                      5293,5292                         5.5,5.5   \n",
       "25                      5292,5291                         5.5,5.5   \n",
       "26                    11193,11193                         7.0,7.0   \n",
       "27                    11185,11185                         7.0,7.0   \n",
       "28                      5291,5290                         5.5,5.5   \n",
       "29                      6997,6997                         6.0,6.0   \n",
       "30                    11191,11191                         7.0,7.0   \n",
       "31                    11193,11193                         7.0,7.0   \n",
       "32                    11192,11192                         7.0,7.0   \n",
       "33                    11190,11190                         7.0,7.0   \n",
       "34                      6995,6995                         6.0,6.0   \n",
       "35                      6985,6985                         6.0,6.0   \n",
       "36                    11189,11189                         7.0,7.0   \n",
       "37                    11191,11191                         7.0,7.0   \n",
       "38                    11191,11191                         7.0,7.0   \n",
       "39                      6994,6994                         6.0,6.0   \n",
       "40                    11189,11189                         7.0,7.0   \n",
       "41                    11191,11191                         7.0,7.0   \n",
       "42                    11192,11192                         7.0,7.0   \n",
       "43                    11192,11192                         7.0,7.0   \n",
       "44                    11181,11181                         7.0,7.0   \n",
       "\n",
       "   Sensitivity, $-\\Delta G_{PFOS}$ [kJ/mol] $\\Delta G_{SDS}$ [kJ/mol]   \n",
       "0                               0.5$\\pm$0.5               0.6$\\pm$0.1  \\\n",
       "1                               1.3$\\pm$0.9              -0.3$\\pm$0.1   \n",
       "2                               2.6$\\pm$0.3               3.8$\\pm$0.1   \n",
       "3                               1.8$\\pm$0.6               1.8$\\pm$0.2   \n",
       "4                              -0.9$\\pm$0.3               0.1$\\pm$0.3   \n",
       "5                               3.9$\\pm$0.2               4.4$\\pm$0.1   \n",
       "6                               2.2$\\pm$0.5               2.5$\\pm$0.2   \n",
       "7                               2.1$\\pm$0.3               1.2$\\pm$0.2   \n",
       "8                               0.4$\\pm$0.4               0.7$\\pm$0.2   \n",
       "9                               1.0$\\pm$0.5               2.2$\\pm$0.3   \n",
       "10                              2.1$\\pm$0.8               2.9$\\pm$0.1   \n",
       "11                              4.3$\\pm$0.6               6.0$\\pm$0.3   \n",
       "12                              1.7$\\pm$0.3               1.6$\\pm$0.2   \n",
       "13                              3.2$\\pm$0.4               2.5$\\pm$0.6   \n",
       "14                              2.4$\\pm$0.2               3.5$\\pm$0.0   \n",
       "15                              3.7$\\pm$0.1               5.2$\\pm$0.1   \n",
       "16                              1.5$\\pm$0.3               2.0$\\pm$0.1   \n",
       "17                              1.4$\\pm$0.3               1.3$\\pm$0.2   \n",
       "18                              5.8$\\pm$0.4               8.0$\\pm$0.5   \n",
       "19                              0.6$\\pm$0.4               1.2$\\pm$0.2   \n",
       "20                              1.8$\\pm$0.1               2.2$\\pm$0.2   \n",
       "21                              1.6$\\pm$0.2               1.4$\\pm$0.3   \n",
       "22                              2.1$\\pm$0.2               1.9$\\pm$0.2   \n",
       "23                              1.6$\\pm$0.5               1.5$\\pm$0.1   \n",
       "24                              2.9$\\pm$0.2               4.2$\\pm$0.1   \n",
       "25                              2.4$\\pm$0.3               4.2$\\pm$0.4   \n",
       "26                              5.0$\\pm$0.4               5.2$\\pm$0.5   \n",
       "27                              4.9$\\pm$0.2               6.7$\\pm$0.5   \n",
       "28                              3.2$\\pm$0.3               4.6$\\pm$0.2   \n",
       "29                              3.8$\\pm$0.0               4.8$\\pm$0.1   \n",
       "30                              3.5$\\pm$0.2               5.2$\\pm$0.5   \n",
       "31                              5.4$\\pm$0.7               5.5$\\pm$0.3   \n",
       "32                              5.2$\\pm$0.4               6.4$\\pm$0.3   \n",
       "33                              5.2$\\pm$0.4               6.3$\\pm$0.5   \n",
       "34                              4.8$\\pm$0.8               5.7$\\pm$0.3   \n",
       "35                              7.0$\\pm$0.2               7.9$\\pm$0.1   \n",
       "36                              5.9$\\pm$0.5               6.7$\\pm$0.8   \n",
       "37                              5.2$\\pm$0.3               5.4$\\pm$0.8   \n",
       "38                              4.5$\\pm$0.8               5.1$\\pm$0.7   \n",
       "39                              4.6$\\pm$0.5               5.6$\\pm$0.1   \n",
       "40                              5.1$\\pm$0.2               6.2$\\pm$0.3   \n",
       "41                              5.4$\\pm$0.2               5.6$\\pm$0.5   \n",
       "42                              4.9$\\pm$0.6               6.6$\\pm$0.3   \n",
       "43                              5.0$\\pm$0.2               5.5$\\pm$0.2   \n",
       "44                              8.4$\\pm$0.5               9.3$\\pm$0.8   \n",
       "\n",
       "   Selectivity, $-\\Delta\\Delta G_{PFOS - SDS}$ [kJ/mol] $K_b^\\mathrm{PFOS}$   \n",
       "0                                        -0.1$\\pm$0.5           4.1$\\pm$0.7  \\\n",
       "1                                         1.5$\\pm$0.9           5.1$\\pm$0.9   \n",
       "2                                        -1.2$\\pm$0.3           8.8$\\pm$0.7   \n",
       "3                                         0.0$\\pm$0.7           6.6$\\pm$1.3   \n",
       "4                                        -1.0$\\pm$0.4           2.6$\\pm$0.2   \n",
       "5                                        -0.6$\\pm$0.2          14.0$\\pm$0.8   \n",
       "6                                        -0.2$\\pm$0.6           8.1$\\pm$1.2   \n",
       "7                                         0.9$\\pm$0.4           7.4$\\pm$0.8   \n",
       "8                                        -0.3$\\pm$0.5           4.0$\\pm$0.4   \n",
       "9                                        -1.2$\\pm$0.6           5.8$\\pm$0.8   \n",
       "10                                       -0.9$\\pm$0.8           7.2$\\pm$1.8   \n",
       "11                                       -1.7$\\pm$0.7          13.2$\\pm$1.9   \n",
       "12                                        0.0$\\pm$0.4           6.3$\\pm$0.5   \n",
       "13                                        0.6$\\pm$0.7          11.5$\\pm$1.6   \n",
       "14                                       -1.1$\\pm$0.2           7.8$\\pm$0.6   \n",
       "15                                       -1.5$\\pm$0.1          12.7$\\pm$0.6   \n",
       "16                                       -0.4$\\pm$0.4           6.4$\\pm$0.6   \n",
       "17                                        0.0$\\pm$0.4           5.9$\\pm$0.7   \n",
       "18                                       -2.2$\\pm$0.6          21.9$\\pm$2.8   \n",
       "19                                       -0.7$\\pm$0.4           4.6$\\pm$0.6   \n",
       "20                                       -0.5$\\pm$0.2           7.3$\\pm$0.1   \n",
       "21                                        0.2$\\pm$0.4           7.2$\\pm$0.3   \n",
       "22                                        0.1$\\pm$0.3           8.3$\\pm$0.7   \n",
       "23                                        0.1$\\pm$0.5           6.7$\\pm$1.1   \n",
       "24                                       -1.3$\\pm$0.2          10.8$\\pm$1.0   \n",
       "25                                       -1.8$\\pm$0.5           9.6$\\pm$1.0   \n",
       "26                                       -0.2$\\pm$0.6          23.0$\\pm$3.5   \n",
       "27                                       -1.7$\\pm$0.6          26.9$\\pm$2.0   \n",
       "28                                       -1.5$\\pm$0.3          12.2$\\pm$1.2   \n",
       "29                                       -1.0$\\pm$0.1          16.4$\\pm$0.4   \n",
       "30                                       -1.7$\\pm$0.5          14.3$\\pm$1.2   \n",
       "31                                       -0.1$\\pm$0.8          27.7$\\pm$6.3   \n",
       "32                                       -1.2$\\pm$0.5          27.1$\\pm$2.8   \n",
       "33                                       -1.1$\\pm$0.6          25.1$\\pm$3.0   \n",
       "34                                       -0.9$\\pm$0.8          23.3$\\pm$6.5   \n",
       "35                                       -0.9$\\pm$0.2          55.6$\\pm$3.3   \n",
       "36                                       -0.8$\\pm$0.9          33.5$\\pm$4.7   \n",
       "37                                       -0.2$\\pm$0.8          28.9$\\pm$3.7   \n",
       "38                                       -0.7$\\pm$1.0          19.0$\\pm$4.4   \n",
       "39                                       -1.0$\\pm$0.5          22.8$\\pm$4.3   \n",
       "40                                       -1.1$\\pm$0.3          23.5$\\pm$1.7   \n",
       "41                                       -0.2$\\pm$0.5          29.7$\\pm$3.1   \n",
       "42                                       -1.7$\\pm$0.7          22.0$\\pm$4.0   \n",
       "43                                       -0.6$\\pm$0.3          23.0$\\pm$1.9   \n",
       "44                                       -1.0$\\pm$0.9         85.8$\\pm$18.6   \n",
       "\n",
       "   $K_b^\\mathrm{SDS}$ $K_b^\\mathrm{PFOS}/K_b^\\mathrm{SDS}$   \n",
       "0         3.7$\\pm$0.2                          1.1$\\pm$0.2  \\\n",
       "1         2.7$\\pm$0.1                          1.9$\\pm$0.3   \n",
       "2        11.3$\\pm$0.7                          0.8$\\pm$0.1   \n",
       "3         5.4$\\pm$0.3                          1.2$\\pm$0.3   \n",
       "4         3.1$\\pm$0.3                          0.8$\\pm$0.1   \n",
       "5        12.2$\\pm$0.3                          1.1$\\pm$0.1   \n",
       "6         7.5$\\pm$0.8                          1.1$\\pm$0.2   \n",
       "7         4.6$\\pm$0.3                          1.6$\\pm$0.2   \n",
       "8         3.8$\\pm$0.2                          1.0$\\pm$0.1   \n",
       "9         6.8$\\pm$0.6                          0.9$\\pm$0.1   \n",
       "10        7.9$\\pm$0.3                          0.9$\\pm$0.2   \n",
       "11       18.6$\\pm$1.7                          0.7$\\pm$0.1   \n",
       "12        4.9$\\pm$0.3                          1.3$\\pm$0.1   \n",
       "13        7.1$\\pm$1.4                          1.6$\\pm$0.4   \n",
       "14       10.3$\\pm$0.2                          0.8$\\pm$0.1   \n",
       "15       15.6$\\pm$0.6                          0.8$\\pm$0.0   \n",
       "16        6.0$\\pm$0.4                          1.1$\\pm$0.1   \n",
       "17        5.1$\\pm$0.4                          1.1$\\pm$0.2   \n",
       "18       43.2$\\pm$8.6                          0.5$\\pm$0.1   \n",
       "19        4.5$\\pm$0.3                          1.0$\\pm$0.1   \n",
       "20        6.9$\\pm$0.6                          1.1$\\pm$0.1   \n",
       "21        5.1$\\pm$0.6                          1.4$\\pm$0.2   \n",
       "22        6.8$\\pm$0.5                          1.2$\\pm$0.1   \n",
       "23        5.7$\\pm$0.3                          1.2$\\pm$0.2   \n",
       "24       14.3$\\pm$0.9                          0.8$\\pm$0.1   \n",
       "25       14.0$\\pm$2.0                          0.7$\\pm$0.1   \n",
       "26       21.3$\\pm$4.2                          1.1$\\pm$0.3   \n",
       "27       45.2$\\pm$9.0                          0.6$\\pm$0.1   \n",
       "28       16.7$\\pm$1.2                          0.7$\\pm$0.1   \n",
       "29       15.9$\\pm$1.1                          1.0$\\pm$0.1   \n",
       "30       16.1$\\pm$2.2                          0.9$\\pm$0.1   \n",
       "31       22.9$\\pm$3.1                          1.2$\\pm$0.3   \n",
       "32       34.5$\\pm$3.2                          0.8$\\pm$0.1   \n",
       "33       28.6$\\pm$5.9                          0.9$\\pm$0.2   \n",
       "34       28.6$\\pm$2.6                          0.8$\\pm$0.2   \n",
       "35       63.1$\\pm$3.8                          0.9$\\pm$0.1   \n",
       "36       33.9$\\pm$8.5                          1.0$\\pm$0.3   \n",
       "37       19.4$\\pm$3.2                          1.5$\\pm$0.3   \n",
       "38       19.1$\\pm$4.6                          1.0$\\pm$0.3   \n",
       "39       24.5$\\pm$0.7                          0.9$\\pm$0.2   \n",
       "40       30.1$\\pm$2.4                          0.8$\\pm$0.1   \n",
       "41       23.2$\\pm$3.7                          1.3$\\pm$0.2   \n",
       "42       31.3$\\pm$4.3                          0.7$\\pm$0.2   \n",
       "43       20.2$\\pm$1.7                          1.1$\\pm$0.1   \n",
       "44     101.5$\\pm$12.5                          0.8$\\pm$0.2   \n",
       "\n",
       "                                        smiles_string  \n",
       "0                                                CCCC  \n",
       "1                                                CCCN  \n",
       "2                                            CCCCCCCC  \n",
       "3                                              CCCCCC  \n",
       "4                                               CCCNC  \n",
       "5                                          CCCCCCCCCC  \n",
       "6                                      CCCC(Cl)(Cl)Cl  \n",
       "7                                         CCCC(F)(F)F  \n",
       "8                                         NCCC(F)(F)F  \n",
       "9                                      CCC[N+](C)(C)C  \n",
       "10                                      CCCCCC(F)(F)F  \n",
       "11                                       CCCCCCCCCCCC  \n",
       "12                                       CNCCC(F)(F)F  \n",
       "13                                 CN(C)CCC(Cl)(Cl)Cl  \n",
       "14                                    CCCCCCCC(F)(F)F  \n",
       "15                                  CCCCCCCCCC(F)(F)F  \n",
       "16                                  CCC(F)(F)C(F)(F)F  \n",
       "17                              C[N+](C)(C)CCC(F)(F)F  \n",
       "18                                CCCCCCCCCCCC(F)(F)F  \n",
       "19                            CC(F)(F)C(F)(F)C(F)(F)F  \n",
       "20                     FC(F)(F)C(F)(F)C(F)(F)C(F)(F)F  \n",
       "21                    CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)F  \n",
       "22              CC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F  \n",
       "23       FC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F  \n",
       "24      CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F  \n",
       "25  CC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F...  \n",
       "26  CCCCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F...  \n",
       "27  CCCCCC(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)...  \n",
       "28  FC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F...  \n",
       "29  CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(...  \n",
       "30  CCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C...  \n",
       "31  CCCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)...  \n",
       "32  CCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C...  \n",
       "33  CCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C...  \n",
       "34  CC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F...  \n",
       "35  CCCC(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(C...  \n",
       "36  CCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C...  \n",
       "37  CCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C...  \n",
       "38  NCCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C...  \n",
       "39  FC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F...  \n",
       "40  CN(C)CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)...  \n",
       "41  CCC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(...  \n",
       "42  CC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F...  \n",
       "43  FC(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F...  \n",
       "44  ClC(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chem_sample_table[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5784d159",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NC(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)Cl',\n",
       "       'CCCCCC(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)N(C)C',\n",
       "       'CN(C)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)Cl',\n",
       "       'CP(C)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br)(Br)Br',\n",
       "       'CN(C)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(C)(Cl)Cl'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catenated_smiles[10][final_round_Pareto_catenated_indices]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6882e403",
   "metadata": {},
   "source": [
    "#### find rows in last cycle that correspond to Pareto frontier probes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "de977ed3",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_pareto_chem_sample_table = pd.DataFrame()\n",
    "for i in range(11):\n",
    "\n",
    "    _final_pareto_rows = chem_sample_table[i][(chem_sample_table[i]['smiles_string'].isin(catenated_smiles[10][final_round_Pareto_catenated_indices]))]\n",
    "\n",
    "    if not _final_pareto_rows.empty:\n",
    "        \n",
    "        final_pareto_chem_sample_table = pd.concat([final_pareto_chem_sample_table, _final_pareto_rows])\n",
    " \n",
    "\n",
    "final_pareto_chem_sample_table.drop_duplicates(inplace=True)\n",
    "#'Sensitivity, $-\\\\Delta G_{PFOS}$ [kJ/mol]','$\\\\Delta G_{SDS}$ [kJ/mol]','Selectivity, $-\\\\Delta\\\\Delta G_{PFOS - SDS}$ [kJ/mol]'\n",
    "\n",
    "select_columns_final_pareto_chem_sample_table =  final_pareto_chem_sample_table[['SMILES',\n",
    "                                                 'Mol. weight [kg/kmol]', 'Structure',\n",
    "                                                 '$K_b^\\mathrm{PFOS}$',\n",
    "                                                 '$K_b^\\mathrm{SDS}$',\n",
    "                                                 '$K_b^\\mathrm{PFOS}/K_b^\\mathrm{SDS}$']].reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "118c0f54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SMILES</th>\n",
       "      <th>Mol. weight [kg/kmol]</th>\n",
       "      <th>Structure</th>\n",
       "      <th>$K_b^\\mathrm{PFOS}$</th>\n",
       "      <th>$K_b^\\mathrm{SDS}$</th>\n",
       "      <th>$K_b^\\mathrm{PFOS}/K_b^\\mathrm{SDS}$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>\\seqsplit{NC(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl...</td>\n",
       "      <td>1116.18</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>99.0$\\pm$67.6</td>\n",
       "      <td>47.9$\\pm$14.7</td>\n",
       "      <td>2.1$\\pm$1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\\seqsplit{CCCCCC(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br...</td>\n",
       "      <td>1303.99</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>62.7$\\pm$15.3</td>\n",
       "      <td>13.6$\\pm$3.8</td>\n",
       "      <td>4.6$\\pm$1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>\\seqsplit{CN(C)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl...</td>\n",
       "      <td>980.33</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>118.3$\\pm$20.8</td>\n",
       "      <td>106.2$\\pm$2.0</td>\n",
       "      <td>1.1$\\pm$0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>\\seqsplit{CP(C)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br...</td>\n",
       "      <td>2008.14</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>177.4$\\pm$12.7</td>\n",
       "      <td>1119.9$\\pm$310.2</td>\n",
       "      <td>0.2$\\pm$0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>\\seqsplit{CN(C)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl...</td>\n",
       "      <td>1042.33</td>\n",
       "      <td>\\begin{minipage}[t]{1\\linewidth} \\includegraph...</td>\n",
       "      <td>129.7$\\pm$45.6</td>\n",
       "      <td>118.5$\\pm$16.1</td>\n",
       "      <td>1.1$\\pm$0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              SMILES Mol. weight [kg/kmol]   \n",
       "0  \\seqsplit{NC(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl...               1116.18  \\\n",
       "1  \\seqsplit{CCCCCC(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br...               1303.99   \n",
       "2  \\seqsplit{CN(C)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl...                980.33   \n",
       "3  \\seqsplit{CP(C)C(Br)(Br)C(Br)(Br)C(Br)(Br)C(Br...               2008.14   \n",
       "4  \\seqsplit{CN(C)C(Cl)(Cl)C(Cl)(Cl)C(Cl)(Cl)C(Cl...               1042.33   \n",
       "\n",
       "                                           Structure $K_b^\\mathrm{PFOS}$   \n",
       "0  \\begin{minipage}[t]{1\\linewidth} \\includegraph...       99.0$\\pm$67.6  \\\n",
       "1  \\begin{minipage}[t]{1\\linewidth} \\includegraph...       62.7$\\pm$15.3   \n",
       "2  \\begin{minipage}[t]{1\\linewidth} \\includegraph...      118.3$\\pm$20.8   \n",
       "3  \\begin{minipage}[t]{1\\linewidth} \\includegraph...      177.4$\\pm$12.7   \n",
       "4  \\begin{minipage}[t]{1\\linewidth} \\includegraph...      129.7$\\pm$45.6   \n",
       "\n",
       "  $K_b^\\mathrm{SDS}$ $K_b^\\mathrm{PFOS}/K_b^\\mathrm{SDS}$  \n",
       "0      47.9$\\pm$14.7                          2.1$\\pm$1.5  \n",
       "1       13.6$\\pm$3.8                          4.6$\\pm$1.7  \n",
       "2      106.2$\\pm$2.0                          1.1$\\pm$0.2  \n",
       "3   1119.9$\\pm$310.2                          0.2$\\pm$0.0  \n",
       "4     118.5$\\pm$16.1                          1.1$\\pm$0.4  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_columns_final_pareto_chem_sample_table # SI FIRST TABLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "39f06bed",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for c in range(11):\n",
    "    with open('../Data/chem_sample_tables/table_final_pareto.tex','w') as tf:\n",
    "        styler = select_columns_final_pareto_chem_sample_table.style.hide(axis='index')\n",
    "        tf.write(styler.to_latex(caption=r'Top performing probes residing on the sensitivity-selectivity Pareto frontier in the terminal round of our active learning campaign.',                                 \n",
    "                       label='table'+str(c), hrules=True,\n",
    "                       environment=\"longtable\",\n",
    "                       column_format='|p{7.cm}|p{1cm}|p{1.2cm}|p{1.2cm}|p{1.25cm}|p{1.6cm}|'))\n",
    "        \n",
    "        #tf.write(styler.to_latex())\n",
    "        #tf.write(chem_sample_table[c].to_latex(index=False,\n",
    "        #                                       caption=r'Probes explored in active learning cycle '+str(c)+'. SMILES strings are generated using RDKit v2022.09.5\\\\cite{greglandrum20237671152}. CAS Registration Number (RN) are generated using cirpy v1.0.2 (\\\\url{/~https://github.com/mcs07/CIRpy.git}) from the SMILES strings. IUPAC names are created using STOUT v2.0.5\\cite{rajan2021stout} using the SMILES strings. Molecular weight and structures of each probe are generated using RDKit v2022.09.5\\cite{greglandrum20237671152} with SMILES strings as inputs.', \n",
    "        #                                       label='table'+str(c), escape=False, bold_rows=True,\n",
    "        #                                       longtable=True,\n",
    "        #                                       column_format='|p{.125cm}|p{1.2cm}|p{.8cm}|p{1.2cm}|p{.9cm}|p{1.75cm}|p{1.4cm}|p{1.cm}|p{1.7cm}|p{2cm}|'))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b92986d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3142e9b8",
   "metadata": {},
   "source": [
    "#### save selected columns in each cycle to text file; SI remaining tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "58df33ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for c in range(11):\n",
    "#    chem_sample_table[0]\n",
    "select_columns_chem_sample_table = []\n",
    "for c in range(11):\n",
    "\n",
    "    select_columns_chem_sample_table.append(chem_sample_table[c][['smiles_string',\n",
    "                                                     'Mol. weight [kg/kmol]',\n",
    "                                                     'Sensitivity, $-\\\\Delta G_{PFOS}$ [kJ/mol]',\n",
    "                                                     '$\\\\Delta G_{SDS}$ [kJ/mol]',\n",
    "                                                     'Selectivity, $-\\\\Delta\\\\Delta G_{PFOS - SDS}$ [kJ/mol]',\n",
    "                                                     '$K_b^\\mathrm{PFOS}$',\n",
    "                                                     '$K_b^\\mathrm{SDS}$',\n",
    "                                                     '$K_b^\\mathrm{PFOS}/K_b^\\mathrm{SDS}$']].reset_index(drop=True))\n",
    "    \n",
    "\n",
    "      \n",
    "    select_columns_chem_sample_table[c].rename(columns={'smiles_string': 'SMILES'}, inplace=True)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "daad6133",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SAVE; if required\n",
    "simdir='../Data/'\n",
    "\n",
    "for c in range(11):\n",
    "    select_columns_chem_sample_table[c].to_csv(simdir+'./JCED_data/cycle-'+str(c)+'.txt',sep='\\t')\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86fce0a4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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